Due to the inaccuracy of logging tools and unpredictable natural variations in geometry and geological parameters, there is considerable uncertainty as to the detailed characterization of a hydrocarbon reservoir. This uncertainty, coupled with the dramatic variations in the market value of hydrocarbon production, has heightened the importance of financial factors and risk management in reservoir strategies so as to maximize reservoir value.
Reservoir risk management involves optimizing assets given inherent reservoir uncertainties and minimizing risk by reducing these uncertainties. Optimizing assets usually involves making decisions about technologies and strategies (such as advanced completion systems, drilling a new well, setting injection or production target rates, etc.) and quantifying the value of implementing the proposed technology in the presence of physical and financial uncertainties. Physical uncertainty includes uncertainty in the type of reservoir model used and the properties used to populate the model. Financial uncertainties refer to uncertainty in the financial variables associated with the asset, such as the discount rate, hydrocarbon price, etc. The optimization process thus should be stochastic, with a risk level (or alternatively a confidence level) associated with the optimized solution. Based on the cost-benefit analysis (cost of implementing the technology versus the gain or value from the implementation) and the associated risk level, a decision may be made on implementing the technology. Minimizing the risk level involves gathering information about the reservoir to reduce or eliminate the inherent uncertainties. The cost of gathering this information should be balanced against the value the information brings to the stochastic optimization process. Valuation of information can then guide decisions on implementing monitoring technologies for uncertainty reduction.
Risk analysis has taken a much more prominent role since the late 1990's when hydrocarbon production costs significantly fluctuated. The oil and gas industry has taken a harder look at the technology available to perform more accurate and more efficient risk analysis. Bailey et al.'s “Taking a Calculated Risk,” Oilfield Review, Vol. 12, No. 3, pages 20-35 (2000) (incorporated by reference herein in its entirety) surveyed the major topics of interest to perform risk analysis at various degrees as well as the role and impact these techniques have on the operators in the petroleum industry. This was further exemplified using an extensive case study of a development offshore in Coopersmith et al.'s “Making Decisions in the Oil and Gas Industry,” Oilfield Review Vol. 12, No. 4, pages 2-9 (2000) (incorporated by reference herein in its entirety). As the oil and gas industry shifts its emphasis from profit by cost-cutting to a diversification of asset-management practices, global portfolio optimization akin to what is prevalent in the financial market industry could potentially be an extremely valuable exercise as shown by Adams et al. in “Portfolio Management for Strategic Growth,” Oilfield Review, Vol. 12, No. 4, pages 10-19 (2000) (incorporated by reference herein in its entirety).
Current industry practice for consideration of uncertainty in oilfield risk analysis typically involves multiple realizations of a given reservoir model (the level of sophistication ranging from simple spreadsheet tank [Material Balance] models to a geologically detailed full-scale model). This approach to reservoir risk analysis is not amenable to true stochastic optimization. For example, the optimum flow rates for wells in a faulted reservoir will depend strongly on the transmissibility of these faults that are often uncertain and can significantly affect the fluid flow in the reservoir. Hence, to obtain the optimum flow rates, one needs to integrate the fault transmissibility uncertainty and its effect on fluid flow into the stochastic optimization routine. This is not feasible with current treatments of uncertainty.
An optimization methodology for deterministic reservoirs (without any uncertainties in reservoir parameters) was applied in the 1980's to determine the optimum injection strategy for surfactants by optimizing the difference between gross revenue and the cost of injection chemicals. This methodology is described by Fathi et al. in “Use of Optimal Control Theory for Computing Optimal Injection Policies for Enhanced Oil Recovery,” Automatica, Vol. 22, pages 33-42 (1984) (incorporated by reference herein in its entirety) and by Ramirez in Applications of Optimal Control Theory to Enhance Oil Recovery (1987) (incorporated by reference herein in its entirety). More recently, commonly owned U.S. Ser. No. 09/930,935 to Couet et al. (the '935 application) and Burridge et al. “Optimal Stimulation of Oil Production,” Decision Making under Uncertainty: Energy and Power, IMA Volumes in Mathematics and Its Applications, Vol. 128, pages 17-37 (2002) (incorporated by reference herein in their entireties) address the extended issue of optimization of oil recovery subject to various physical uncertainties (stochastic optimization) and minimization of downside risk.
While these studies describe algorithms for stochastic optimization of reservoirs, they do not provide methodologies for formulating objective functions suitable for decision-making. As will be shown below, useful methodologies should allow for decisions based on a cost-benefit analysis for a technology implementation or choosing between technologies in the presence of uncertainties. This requires direct optimization of the difference between the value of the management goal (such as net present value, hydrocarbon production etc.) obtained when the technology is implemented and the value of the goal obtained for a reference case without that technology. The optimization of this difference or gain in value of a goal in accordance with the present invention is in contrast to the traditional approach that only considers the difference between the individually optimized values of the goal for the two cases. The results can be very different for the two approaches as values of the goal obtained for both the technology and reference cases are dependent on the inherent reservoir uncertainties. Accordingly, there is a need for a method that accounts for the affect of inherent reservoir uncertainties, such as in cost-benefit analysis. Furthermore, there are no existing methodologies to account for the gain in value of a goal associated with the reduction in uncertainty due to acquisition of new information, which can be defined as the value of information.
Accordingly, it is an object of the present invention to provide methods for decision-making in reservoir risk management that are stochastic and account for the gain in value attributable to one or more hydrocarbon exploitation strategies/technologies, wherein this gain in value of the goal is defined as the value of that strategy/technology.
It is yet another object of the present invention to provide decision-making methods which further account for one or multiple uncertainties in reservoir properties.
It is also an object of the present invention to provide methods that allow the quantification of the value of information in reservoir risk management.